Enhancing Monte Carlo Dropout Performance for Uncertainty Quantification

2025-05-21

AI Summary

To address the poor uncertainty calibration of Monte Carlo Dropout (MCD) in high-stakes applications—such as medical diagnosis and autonomous driving—this paper proposes a framework integrating hyperparameter co-optimization and loss-function enhancement. We innovatively introduce, for the first time in MCD, three complementary global optimizers—Grey Wolf Optimizer (GWO), Bayesian Optimization (BO), and Particle Swarm Optimization (PSO)—to jointly tune dropout rate, network depth, and temperature scaling. Concurrently, we design an uncertainty-aware loss function that explicitly penalizes miscalibrated confidence estimates. Evaluated across DenseNet121, ResNet50, and VGG16 backbones on multiple benchmark datasets, our method improves both classification accuracy and uncertainty accuracy by 2–3% on average, while significantly reducing Expected Calibration Error (ECE). Results demonstrate that the approach preserves predictive performance while substantially enhancing the reliability and robustness of uncertainty quantification—making it suitable for safety-critical deep learning deployments.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Reasoning under Uncertainty: Stochastic Optimization
• Computer Vision: Learning & Optimization for CV

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs

Abstract

Knowing the uncertainty associated with the output of a deep neural network is of paramount importance in making trustworthy decisions, particularly in high-stakes fields like medical diagnosis and autonomous systems. Monte Carlo Dropout (MCD) is a widely used method for uncertainty quantification, as it can be easily integrated into various deep architectures. However, conventional MCD often struggles with providing well-calibrated uncertainty estimates. To address this, we introduce innovative frameworks that enhances MCD by integrating different search solutions namely Grey Wolf Optimizer (GWO), Bayesian Optimization (BO), and Particle Swarm Optimization (PSO) as well as an uncertainty-aware loss function, thereby improving the reliability of uncertainty quantification. We conduct comprehensive experiments using different backbones, namely DenseNet121, ResNet50, and VGG16, on various datasets, including Cats vs. Dogs, Myocarditis, Wisconsin, and a synthetic dataset (Circles). Our proposed algorithm outperforms the MCD baseline by 2-3% on average in terms of both conventional accuracy and uncertainty accuracy while achieving significantly better calibration. These results highlight the potential of our approach to enhance the trustworthiness of deep learning models in safety-critical applications.

Problem

• Improving uncertainty quantification in deep neural networks
• Enhancing Monte Carlo Dropout calibration for reliable decisions
• Optimizing uncertainty estimates in high-stakes applications like healthcare

Innovation

• Integrates Grey Wolf Optimizer for MCD enhancement
• Uses Bayesian Optimization to improve uncertainty estimates
• Applies Particle Swarm Optimization for better calibration